Abstract
Objective: To design ARMA model for the analysis of histopathology images.
Background: In traditional cancer diagnosis using clinical pathology, pathologists examine biopsy samples and analyzes it on the basis of cell structure and tissue arrangement. This has unpredictability in ontogenesis and in positioning. Methods: These methods have been replaced by computer assisted diagnostics (CAD) that support impartial judgment. This work demonstrates the influence of 2D ARMA models in the analysis and classification of histopathology images. The parameter estimation has been done with Yule walker Least Square (LS) method. This work describes brain histopathology image by ARMA parameters which is further analyzed and classified. These features are classified into healthy and malignant tissue samples. The liner kernel and RBF kernel support vector machine (SVM) classifier and Fusion of both classifiers have been used for diagnosis. Results: Results show that estimated ARMA parameters are excellent discriminating features for statistical study of histopathology images and valuable in cancer diagnostics. Total accuracy improvement is shown by fusing the out puts of linear kernel and RBF kernel classifier with Bayes and Decision template fusion schemes. Conclusion: This work describes new approach of using ARMA features to extract hidden information in histopathology imagery.Keywords: Autoregressive model and moving average (ARMA), least square (LS), quarter plane (QP), markov random field model (MRF), radial basis function (RBF), support vector machine (SVM).
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